This document specifies version 1.3 of the Transport Layer Security
(TLS) protocol. TLS allows client/server applications to communicate
over the Internet in a way that is designed to prevent eavesdropping,
tampering, and message forgery. This document updates RFCs 4492, 5705,
and 6066 and it obsoletes RFCs 5077, 5246, and 6961. This document
also specifies new requirements for TLS 1.2 implementations.

The Transport Layer Security (TLS) Protocol Version 1.3

In this paper (expanded from an invited talk at AISEC 2010), we discuss an emerging field of study: adversarial machine learning---the study of effective machine learning techniques against an adversarial opponent. In this paper, we: give a taxonomy for classifying attacks against online machine learning algorithms; discuss application-specific factors that limit an adversary's capabilities; introduce two models for modeling an adversary's capabilities; explore the limits of an adversary's knowledge about the algorithm, feature space, training, and input data; explore vulnerabilities in machine learning algorithms; discuss countermeasures against attacks; introduce the evasion challenge; and discuss privacy-preserving learning techniques.

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Adversarial machine learning

The author briefly introduces the emerging field of adversarial machine learning, in which opponents can cause traditional machine learning algorithms to behave poorly in security applications. He gives a high-level overview and mentions several types of attacks, as well as several types of defenses, and theoretical limits derived from a study of near-optimal evasion.

/pdf/adversarial-machine-learning-2sref192q1.pdf

Adversarial Machine Learning

Each month, more attacks are launched with the aim of making web users believe that they are communicating with a trusted entity for the purpose of stealing account information, logon credentials, and identity information in general. This attack method, commonly known as "phishing," is most commonly initiated by sending out emails with links to spoofed websites that harvest information. We present a method for detecting these attacks, which in its most general form is an application of machine learning on a feature set designed to highlight user-targeted deception in electronic communication. This method is applicable, with slight modification, to detection of phishing websites, or the emails used to direct victims to these sites. We evaluate this method on a set of approximately 860 such phishing emails, and 6950 non-phishing emails, and correctly identify over 96% of the phishing emails while only mis-classifying on the order of 0.1% of the legitimate emails. We conclude with thoughts on the future for such techniques to specifically identify deception, specifically with respect to the evolutionary nature of the attacks and information available.

/pdf/learning-to-detect-phishing-emails-19e61vbod6.pdf

Learning to detect phishing emails

Recommender systems have become an important research field since the emergence of the first paper on collaborative filtering in the mid-1990s. Although academic research on recommender systems has increased significantly over the past 10years, there are deficiencies in the comprehensive literature review and classification of that research. For that reason, we reviewed 210 articles on recommender systems from 46 journals published between 2001 and 2010, and then classified those by the year of publication, the journals in which they appeared, their application fields, and their data mining techniques. The 210 articles are categorized into eight application fields (books, documents, images, movie, music, shopping, TV programs, and others) and eight data mining techniques (association rule, clustering, decision tree, k-nearest neighbor, link analysis, neural network, regression, and other heuristic methods). Our research provides information about trends in recommender systems research by examining the publication years of the articles, and provides practitioners and researchers with insight and future direction on recommender systems. We hope that this paper helps anyone who is interested in recommender systems research with insight for future research direction.

A literature review and classification of recommender systems research

Much controversy exists over the wisdom and usefulness of the Author Domain Signing Practices (ADSP) standard. ADSP publishes the signing practices that organizations adopt when using DomainKeys Identified Mail (DKIM) to relay mail for associated authors. This article considers the pros and cons of ADSP, with each side written by a participant in the development of the DKIM and ADSP standards.

Author Domain Signing Practices (ADSP): Point and Counterpoint

This document describes a profile of the Sieve extension for
notifications, to allow notifications to be sent by electronic mail.

Sieve Notification Mechanism: mailto

The IMAP4 specification allows the searching of only the selected
mailbox. A user often wants to search multiple mailboxes, and a client
that wishes to support this must issue a series of SELECT and SEARCH
commands, waiting for each to complete before moving on to the next.
This extension allows a client to search multiple mailboxes with one
command, limiting the delays caused by many round trips and not
requiring disruption of the currently selected mailbox. This extension
also uses MAILBOX, UIDVALIDITY, and TAG fields in ESEARCH responses,
allowing a client to pipeline the searches if it chooses. This
document updates RFC 4466 and obsoletes RFC 6237.

IMAP4 Multimailbox SEARCH Extension

Secure/Multipurpose Internet Mail Extensions (S/MIME) defined the
Content-Type parameter "smime-type". As the list of defined values for
that parameter has increased, it has become clear that a registry is
needed to document these values. This document creates the registry,
registers the current values, and specifies the policies for
registration of new values.

Creation of a Registry for smime-type Parameter Values

This document defines a new Abuse Reporting Format (ARF) feedback
report type value: "not-spam". It can be used to report a message that
was mistakenly marked as spam.

Barry Leiba

Papers

Author Domain Signing Practices (ADSP): Point and Counterpoint

Sieve Notification Mechanism: mailto

IMAP4 Multimailbox SEARCH Extension

Creation of a Registry for smime-type Parameter Values

Email Feedback Report Type Value: not-spam